Data Requirements for a Reliable Demand Decomposition in Sparsely Monitored Power Networks

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    Abstract

    This paper discusses data requirements for an efficient demand decomposition at the aggregation level considering a limited number of monitoring points. Two methods are compared: an artificial neural network (ANN) based method and the autoregressive integrated moving average (ARIMA) method, followed by the validation of the superior approach against the data coming from an actual pilot site. The influence of data types, such as the weather and type of day, is investigated, as well as the size of the historical data required. The analysis concludes that the ANN based approach is superior, and that using appropriately trained ANN, even with only 5% of end-users whose per-appliance consumption is being monitored, it is possible to estimate or predict, with high accuracy, the demand composition of the overall aggregation of users.
    Original languageEnglish
    Title of host publication 2018 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe)
    DOIs
    Publication statusPublished - 2018
    Event 2018 IEEE PES Innovative Smart Grid Technologies Conference Europe - Sarajevo, Bosnia-Herzegovina, Sarajevo, Bosnia and Herzegovina
    Duration: 21 Oct 201825 Oct 2018

    Conference

    Conference 2018 IEEE PES Innovative Smart Grid Technologies Conference Europe
    Abbreviated titleISGT-Europe
    Country/TerritoryBosnia and Herzegovina
    CitySarajevo
    Period21/10/1825/10/18

    Keywords

    • Demand side management
    • load forecasting
    • neural networks
    • smart meter
    • time series

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